CN115144753A - Echelon energy storage battery operation safety detection system and method and energy storage system - Google Patents
Echelon energy storage battery operation safety detection system and method and energy storage system Download PDFInfo
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Abstract
The invention discloses a system and a method for detecting the running safety of a echelon energy storage battery and an energy storage system, wherein at least three surfaces of each battery cell are provided with a deformation sensing unit, an electrode end is provided with a voltage sensing unit, and a battery module or a battery cluster is taken as a unit configuration judging unit to form a detection system for each battery unit; the cell deformation parameters are used as feature vectors, a Gaussian regression model is adopted to perform cluster analysis on each battery unit, whether all the battery units still belong to the same category or not is judged, if yes, the battery units are normal, and if not, an abnormal alarm is given; after each judgment, recalculating the relevant parameters of the Gaussian mixture model according to the new deformation dynamic characteristics, and providing initial parameters for next clustering analysis; the battery units in the same echelon energy storage system are used as comparison objects, and real-time monitoring is carried out on the basis of respective actual operation conditions, so that the operation safety monitoring of the battery units is more reliable, accurate and effective; and timely alarming is carried out on the battery unit with abnormal deformation.
Description
[ technical field ] A method for producing a semiconductor device
The invention belongs to the technical field of battery packs, and particularly relates to a system and a method for detecting the running safety of a echelon energy storage battery and an energy storage system.
[ background of the invention ]
With the increasing market reserves of domestic new energy automobiles, the power battery for the automobile can meet the continuously increasing retirement peak in the coming years. The residual capacity of most of the ex-service power batteries for vehicles can still reach 80% of the original capacity. In order to promote the development of circular economy, the retired power battery is applied to an energy storage system with low requirements on battery performance in a gradient utilization mode, the life cycle cost of the battery is reduced, the utilization rate of battery materials is improved, the environmental pollution is reduced, and the method has important significance in promoting the complete closed loop construction of a new energy automobile industry.
Because monomer electricity core voltage is too low, in echelon energy storage system, in order to provide sufficient voltage for equipment, generally need to establish ties into battery module with a lot of electric cores and use, but the performance mismatch between the echelon energy storage battery can influence the performance of whole battery module. The difference may cause overcharge and overdischarge of individual echelon energy storage batteries, reduce the utilization rate of battery energy in an energy storage system, greatly reduce the service life of the echelon energy storage batteries, and possibly cause safety accidents such as ignition or explosion in severe cases.
Most of the current retired lithium ion power batteries are subjected to processes of disassembly, detection, screening, recombination and the like before entering the echelon energy storage system, but many index parameters of the echelon energy storage batteries still generate inconsistent conditions in the operation process, and the operation monitoring at the moment is greatly different from an energy storage battery system formed by a brand-new battery in a group. Currently, most of the monitoring on the operation safety of the series battery pack is designed based on a battery system formed by brand-new batteries, and only the voltage and the temperature of a battery core and the current of the whole battery are monitored, for example, a method, a system and a device for establishing a battery health degree evaluation model and evaluation are disclosed in patent publication No. CN114325447A, and the method is not completely applicable to echelon energy storage batteries. When the echelon energy storage battery is before a safety accident, the battery can generate a serious swelling phenomenon, and the disconnection, the ignition or the explosion of the battery can be sensed in advance based on the swelling phenomenon. Therefore, the necessity of sensing the bulging phenomenon of the battery has appeared, so a perfect, real-time and effective method is needed to detect the state of the echelon energy storage battery, and meanwhile, the running condition of the echelon energy storage battery can be alarmed and protected, accurate and real-time monitoring can be carried out, proper analysis can be made, effective measures can be rapidly adopted, and the method has important significance for ensuring safe, effective and lasting running of the echelon energy storage system and avoiding property loss.
[ summary of the invention ]
The invention mainly aims to provide a method for detecting the running safety of a echelon energy storage battery, which can reliably, accurately and real-timely monitor the running condition of the echelon energy storage battery and ensure the stable running and system safety of an out-of-service lithium ion power battery entering a echelon energy storage utilization link.
The invention realizes the purpose through the following technical scheme: a echelon energy storage battery operation safety detection method comprises the following steps:
s1) building a echelon energy storage system: selecting cells with deformation variation characteristics belonging to the same class to form a battery module, forming a battery cluster by a plurality of battery modules, and forming a echelon energy storage system by a plurality of battery clusters;
s2) calculating deformation parameters of each battery cell:
s21) acquiring deformation data of at least three surfaces of each battery cell respectively: c x1 、C x2 、C x3 ；
S22) distributing corresponding weight coefficients, which are respectively k, to the deformation data according to whether electrodes exist on the surface of the electric core corresponding to the deformation data 1 、k 2 、k 3 ；
S23) deformation parameter C of battery cell x x Comprises the following steps:
s3) calculating deformation parameters of all battery cells in the battery unit t:
wherein N is the total number of the battery cells in the battery unit;
s4) calculating the dynamic change characteristic vector x of the M-dimensional deformation quantity of the battery unit t t ：
S41) constructing a deformation parameter curve according to the deformation parameters obtained in the step S3), and dividing the deformation parameter curve into M segments by taking the cell voltage variation delta U = U as a step length;
s42) calculating the deformation parameter slope of each segment to be
S43) the M-dimensional deformation dynamic change characteristic vector of the battery unit is x t ＝[σ 1 ,σ 2 ,…,σ M ]T =1,2, \8230, where T is the total number of the battery cells;
s5) carrying out clustering analysis on the dynamic change characteristic vectors of the M-dimensional deformation corresponding to all the battery units by adopting a Gaussian regression model to obtain T classification results and judging whether abnormity exists or not;
s51) calculating the deformation dynamic change characteristic vector x corresponding to the battery unit t t Posterior probability P (q) belonging to class k Gaussian curve tk ＝1|x t )：
wherein ,P(xt |μ j ,Σ j ) Is a multi-element Gaussian distribution and has the characteristics of high temperature,
at the initial state, n k 、μ k 、Σ k Respectively obtaining a mixing coefficient, a mean vector and a covariance matrix corresponding to parameters of K Gaussian mixture models of the echelon energy storage system constructed in the step S1) in the component volumetric screening process;
s52) judges whether or not it belongs to the kth class: when q is tk If =1, the characteristic vector x represents the dynamic change of the amount of deformation of the battery cell t t Belongs to the kth category; otherwise, the classification does not belong to the kth classification;
s53) repeating the steps S51) to S52) to obtain the classification results of the T battery units, wherein if the classification results of all the battery clusters are the same, the classification results are normal; if different classification results exist, alarming is carried out on the corresponding battery unit;
s6) recalculating a new mean vector mu according to the result of the step S5) k Coefficient of mixing pi k Covariance matrix sigma k As initial parameters in step S5); wherein,
and S7) repeating the steps S2) to S6), and continuously monitoring whether the battery units in the echelon energy storage system are abnormal or not.
Furthermore, the battery unit is a battery cluster or a battery module.
Further, the weight coefficient of the deformation data is equal to the ratio of the distribution area to the total area of the battery core.
Further, the calculation method of the distribution area comprises the following steps: if no electrode exists on the surface of the corresponding electric core of the deformation data, calculating the distribution area according to 1 time of the area; if one electrode exists, the distribution area is calculated according to 2 times; if there are two electrodes, the dispensing area is calculated as a factor of 4.
Further, in the above-mentioned case,
wherein ,L1 The cell width; l is 2 The cell thickness; l is 3 The length of the battery cell;
further, in the step S41), M is:
another objective of the present invention is to provide a system for detecting the operation safety of a echelon energy storage battery, which comprises
The deformation sensing unit is coupled on at least three surfaces of each battery cell and is used for detecting deformation quantities of each battery cell and among the battery cells;
the voltage sensing unit is used for detecting the voltage of the positive electrode end and the negative electrode end of each battery cell; and
the judging unit is used for comprehensively judging whether the echelon energy storage system has deformation abnormality in the operation process; the deformation sensing units are electrically connected with the judging unit after being connected in series, and the voltage value acquired by the voltage sensing unit is uploaded to the judging unit; the program algorithm for realizing the detection method is written in the judgment unit.
Further, the three surfaces include surfaces on which positive and negative electrodes of the battery cell are located.
Further, the three surfaces are selected from three of four surfaces around the battery cell and comprise surfaces on which positive and negative electrodes of the battery cell are positioned; the deformation sensing units on the same battery core are arranged on the same height plane.
The invention also aims to provide a echelon energy storage system, which comprises the detection system, wherein each battery cell is provided with three deformation sensing units and two voltage sensing units; a plurality of battery modules are assembled to form a battery module, and each battery module is matched with one judgment unit; a plurality of battery modules form a battery cluster, and the plurality of battery clusters form the echelon energy storage system; the cathodes of all the battery modules are gathered to form a total battery negative electrode, the anodes of all the battery modules are gathered to form a total battery positive electrode, and the total battery negative electrode and the total battery positive electrode are connected to the corresponding connecting ends of the energy storage converter; all the determination units are electrically connected to an energy management system and communicate with the energy management system.
Compared with the prior art, the echelon energy storage battery operation safety detection system, the echelon energy storage battery operation safety detection method and the echelon energy storage system have the beneficial effects that: arranging deformation sensing units on at least three surfaces of each battery cell in the echelon energy storage system, arranging voltage sensing units at electrode ends, and configuring a judging unit by taking a battery module or a battery cluster as a unit to form a detection system for each battery unit; based on the cell deformation parameters as dynamic characteristic vectors, a Gaussian regression model is adopted to perform cluster analysis on each battery cluster, after each charge-discharge cycle operation, whether all the battery clusters still belong to the same class is judged, if the battery clusters belong to the same class, the cell operates normally in the energy storage system, and if some battery clusters are not similar to most other battery clusters, the battery clusters are judged to be abnormal; meanwhile, after each judgment, the relevant parameters of the Gaussian mixture model are recalculated according to the new shape variable dynamic characteristic vector, and initial parameters are provided for the next Gaussian regression cluster analysis; the battery clusters or battery modules in the same echelon energy storage system are used as comparison objects, and real-time monitoring is carried out on the basis of respective actual running conditions, so that the running safety monitoring of the battery clusters or the battery modules is more reliable, accurate and effective; the abnormal battery cluster or battery module of deformation is timely reported to the police, ensures that the lithium ion power battery of retirement enters into the steady operation and the system safety of echelon energy storage utilization link.
[ description of the drawings ]
FIG. 1 is a schematic flow diagram of an embodiment of the present invention;
fig. 2 is a schematic diagram of a configuration structure of a deformation sensing unit on a battery cell according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating a connection structure between a deformation sensing unit and a determining unit according to an embodiment of the present invention;
fig. 4 is one of schematic structural diagrams of the arrangement of the voltage sensing unit on the battery cell in the embodiment of the present invention;
fig. 5 is a second schematic view of a configuration of the voltage sensing unit on the battery cell according to the embodiment of the present invention;
FIG. 6 is a diagram illustrating a connection structure between a voltage sensing unit and a determining unit according to an embodiment of the present invention;
FIG. 7 is a second diagram of the connection structure between the voltage sensing unit and the determining unit according to the embodiment of the present invention;
FIG. 8 is a schematic diagram illustrating an arrangement of a detection system in a battery module according to an embodiment of the present invention;
FIG. 9 is a second schematic view illustrating an arrangement structure of the detection system in the battery module according to the embodiment of the present invention;
fig. 10 is a schematic structural diagram of an echelon energy storage system according to an embodiment of the invention.
[ detailed description ] embodiments
The first embodiment is as follows:
referring to fig. 1 to 10, a system for detecting operation safety of an echelon energy storage battery in this embodiment includes: the deformation sensing unit is coupled to the surfaces of the battery cells in the battery module and used for detecting deformation quantities of the battery cells and among the battery cells; the voltage sensing unit is used for detecting the voltage of each battery cell in the battery module; and the judging unit is used for comprehensively judging whether the potential safety hazard exists in the operation process of the energy storage battery module.
The deformation sensing units C are coupled on at least three surfaces of each battery cell 1 、C 2 、C 3 And the three surfaces comprise the surfaces of one side where the positive and negative electrodes of the battery core are positioned. The three surfaces are selected from three of four surfaces around the cell. The deformation sensing unit is selected from an optical fiber sensor, a displacement sensor or a surface deformation sensor. The deformation sensing units on the same battery cell are preferably arranged on the same height plane. For a battery module consisting of battery cores with positive and negative electrodes on the same side, the deformation sensing units are arranged on the electrode side and the other two surfaces; and for the battery module consisting of the battery cells with the positive and negative electrodes positioned on different sides, the deformation sensing units are arranged on two electrode sides and the other surface.
The electrode end of each battery cell is provided with one voltage sensing unit U 1 、U 2 And the voltage sensor is used for detecting the electrode voltage of the battery cell in the battery module. The voltage sensing unit is selected from a sampling resistor, a Hall voltage sensor or a voltage sensor. For a battery module consisting of cells with positive and negative electrodes on the same side, the voltage sensing unit is arranged at the positive and negative electrodes; and for the battery module consisting of the battery cores with the positive and negative electrodes positioned on different sides, the voltage sensing units are respectively arranged at the two electrodes.
All the deformation sensors and all the voltage sensing units are electrically connected to the judging unit, and in the judging unit, the voltage difference between the positive electrode and the negative electrode of each battery cell is calculated according to data acquired by the voltage sensing units.
This embodiment also provides a echelon energy storage system, and it includes: each battery cell is provided with three deformation sensing units and two voltage sensing units; a plurality of battery cores are assembled to form a battery module, and each battery module is matched with one judgment unit; a plurality of battery modules constitute a battery cluster, and a plurality of the battery cluster constitutes echelon energy storage system. The cathodes of all the battery modules are gathered to form a total battery negative electrode, the anodes of all the battery modules are gathered to form a total battery positive electrode, and the total battery negative electrode and the total battery positive electrode are connected to the corresponding connecting ends of the energy storage converter; all the determination units are electrically connected to an energy management system and communicate with the energy management system.
And the battery cell of the echelon energy storage system is obtained by adopting a Gaussian regression model to perform clustering screening.
The embodiment also provides a method for detecting the running safety of the echelon energy storage battery, which comprises the following steps:
s1) building a echelon energy storage system: in order to ensure the consistency of the echelon energy storage system, in the grouping and capacity-grading screening process of the echelon energy storage system, the dynamic change characteristics of the deformation quantity of the battery cell are subjected to clustering analysis through a Gaussian regression model, and then N battery cells belonging to the same class are selected to form a battery module; forming the S battery modules into a battery cluster, and forming the T battery clusters into the echelon energy storage system;
s2) calculating deformation parameters of each battery cell: all deformation sensing units are connected with the judging unit after being connected in series in the same battery module, and every section of electric core x all has 3 deformation sensing units, and its collection value is respectively: c x1 、C x2 、C x3 According to the ratio of the area (the surface without the electrode is calculated according to 1 time of the area; the surface with 1 electrode is calculated according to 2 times of the area; the surface with 2 electrodes is calculated according to 4 times of the area) of the surface on which each deformation sensing unit is arranged to the total area of the battery cell, the weight coefficients corresponding to 3 deformation sensing units are respectively k 1 、k 2 、k 3 ：
wherein ,L1 The cell width; l is 2 The cell thickness; l is 3 The length of the battery cell; the deformation parameters of the cell x are as follows:
s3) calculating deformation parameters of all the battery cells in the battery cluster t: the battery module group formed by N battery cores can obtain N battery core deformation parameters:
a battery module group consisting of S N battery cells forms a battery cluster, and S multiplied by N battery cell deformation parameters can be obtained:
s4) calculating the dynamic change characteristic vector x of the M-dimensional deformation quantity of the battery cluster t t ：
S41) constructing a deformation parameter curve according to the S multiplied by N cell deformation parameters, dividing the deformation parameter curve into M segments by taking the cell voltage change quantity delta U = U as a step length (such as 0.1V), wherein,
m = (3.7V-2.7V)/0.1v =10 in this example;
s42) calculating the deformation parameter slope of each segment to be
Then the M =10 dimensional deformation dynamic change feature vector of the battery cluster t is x t ＝[σ 1 ,σ 2 ,…,σ M ]，t＝1,2,…,T；
S5) dynamic variation characteristic vector x of M dimensional deformation quantity corresponding to T battery clusters t Performing clustering analysis by adopting a Gaussian regression model to obtain classification results of the T battery clusters and judging whether abnormality exists;
s51) calculating a deformation quantity dynamic change characteristic vector x t Posterior probability P (q) belonging to class k Gaussian curve tk ＝1|x t )：
wherein ,P(xt |μ j ,Σ j ) Is a multi-element Gaussian distribution, and has the characteristics of high temperature,
at the initial state, pi k 、μ k 、Σ k Respectively obtaining a mixing coefficient, a mean vector and a covariance matrix corresponding to parameters of K Gaussian mixture models of the echelon energy storage system constructed in the step S1) in the component volumetric screening process;
s52) judging whether or not it belongs to the k-th class: when q is tk If =1, the characteristic vector x represents the dynamic change of the deformation amount of the battery cluster t t Belongs to the kth category; otherwise, the classification does not belong to the kth classification;
s53) repeating the steps S51) to S52) to obtain the classification results of the T battery clusters, and if the classification results of all the battery clusters are the same, the classification results are normal; if different classification results exist, alarming is carried out on the corresponding battery cluster;
s6) recalculating a new mean vector mu according to the result of the step S5) k Mixing coefficient pi k Covariance matrix sigma k Updating and iterating as the initial parameter in the step S5); wherein,
and S7) repeating the steps S2) to S6), and continuously monitoring whether the battery cluster in the echelon energy storage system is abnormal or not.
And continuously performing cluster analysis on the dynamic change characteristics of the echelon energy storage battery in the operation process through a Gaussian regression model, monitoring whether the dynamic change trends of the battery module in the echelon energy storage system in the operation process are consistent or not, and iteratively updating parameters so as to monitor the deformation change of the retired battery in the operation process.
In the running process of the echelon energy storage system, the echelon energy storage batteries continue to iterate for each battery when performing charge-discharge cycle running according to an existing strategy. When the deformation parameter characteristics of all the batteries in the echelon energy storage system in the operation process still belong to the same classification, the operation process is normal; when the deformation parameter characteristics of the battery are different and do not belong to the same classification with the deformation parameter characteristics of other batteries, the deformation abnormality of the battery module is indicated in the operation process, and a system alarms.
In the system, the method and the energy storage system for detecting the running safety of the echelon energy storage battery, deformation sensing units are arranged on at least three surfaces of each battery cell in the echelon energy storage system, voltage sensing units are arranged at electrode ends, and a determination unit is configured by taking a battery module or a battery cluster as a unit to form a detection system for each battery module or battery cluster; based on the cell deformation parameters as dynamic characteristic vectors, a Gaussian regression model is adopted to perform cluster analysis on each battery cluster, after each charge-discharge cycle operation, whether all the battery clusters still belong to the same class is judged, if the battery clusters belong to the same class, the cell operates normally in the energy storage system, and if some battery clusters are not similar to most other battery clusters, the battery clusters are judged to be abnormal; meanwhile, after each judgment, the related parameters of the Gaussian mixture model are recalculated according to the new deformation dynamic characteristic vector, and initial parameters are provided for the next Gaussian regression clustering analysis; the battery clusters in the same echelon energy storage system are used as comparison objects, and real-time monitoring is carried out based on respective actual running conditions, so that the running safety monitoring of the battery clusters is more reliable, accurate and effective; the abnormal deformation of the battery cluster or the battery module is timely alarmed, and the stable operation and the system safety of the retired lithium ion power battery entering the echelon energy storage utilization link are ensured.
Example two:
the embodiment is a method for detecting the operation safety of an echelon energy storage battery, which is basically the same as the detection method in the first embodiment, and the difference is that: in the embodiment, the battery modules are taken as units, each battery module in the same battery cluster is compared and analyzed, and an alarm is given to the abnormal battery modules. Specifically, the method comprises the following steps:
s1) building a echelon energy storage system: in order to ensure the consistency of the echelon energy storage system, in the grouping and capacity-grading screening process of the echelon energy storage system, the dynamic change characteristics of the deformation quantity of the battery cell are subjected to clustering analysis through a Gaussian regression model, and then N battery cells belonging to the same class are selected to form a battery module; forming the S battery modules into a battery cluster, and forming the T battery clusters into the echelon energy storage system;
s2) calculating deformation parameters of each battery cell: all deformation sensing units are connected with the decision unit after establishing ties in same battery module, and every section electricity core x all has 3 deformation sensing units, and its collecting value is respectively: c x1 、C x2 、C x3 According to the ratio of the area (the surface without the electrode is calculated according to 1 time of the area; the surface with 1 electrode is calculated according to 2 times of the area; the surface with 2 electrodes is calculated according to 4 times of the area) of the surface on which each deformation sensing unit is arranged to the total area of the battery cell, the weight coefficients corresponding to 3 deformation sensing units are respectively k 1 、k 2 、k 3 ：
wherein ,L1 The cell width; l is 2 The cell thickness is taken as the cell thickness; l is 3 The length of the battery cell; the deformation parameters of the cell x are as follows:
s3) calculating deformation parameters of all battery cells in the battery module S: a battery module consisting of N battery cells can obtain N battery cell deformation parameters:
s4) calculating the dynamic change characteristic vector x of the M-dimensional deformation quantity of the battery module S s ：
S41) constructing a deformation parameter curve according to the N cell deformation parameters, dividing the deformation parameter curve into M segments by taking the cell voltage variation delta U = U as a step length (such as 0.1V), wherein,
m = (3.7V-2.7V)/0.1v =10 in this example;
s42) calculating the deformation parameter slope of each segment to be
Then the M =10 dimensional deformation dynamic change feature vector of the battery cluster t is x s ＝[σ 1 ,σ 2 ,…,σ M ]，s＝1,2,…,S；
S5) calculating M-dimensional deformation dynamic change characteristic vectors x corresponding to S battery modules s Performing clustering analysis by adopting a Gaussian regression model to obtain classification results of the S battery modules, and judging whether abnormalities exist or not;
s51) calculating a deformation quantity dynamic change characteristic vector x s Posterior probability P (q) belonging to class k Gaussian curve sk ＝1|x s )：
wherein ,P(xs |μ j ,Σ j ) Is a multi-element Gaussian distribution, and has the characteristics of high temperature,
at the initial state, pi k 、μ k 、Σ k Respectively obtaining a mixing coefficient, a mean vector and a covariance matrix corresponding to parameters of K Gaussian mixture models of the echelon energy storage system constructed in the step S1) in the component capacity screening process;
s52) judging whether or not it belongs to the k-th class: when q is sk If =1, the characteristic vector x represents the dynamic variation of the deformation amount of the battery module s s Belongs to the kth category; otherwise, the classification does not belong to the kth classification;
s53) repeating the steps S51) to S52) to obtain the classification results of the S battery modules, wherein if the classification results of all the battery modules are the same, the classification results are normal; if different classification results exist, alarming the corresponding battery module;
6) Recalculating a new mean vector μ based on the result of step S5) k Mixing the components coefficient pi k Covariance matrix sigma k Updating and iterating as the initial parameter in the step S5); wherein,
7) And repeating the step S2) -the step S6), and continuously monitoring whether the battery module in the echelon energy storage system is abnormal.
What has been described above are merely some embodiments of the present invention. It will be apparent to those skilled in the art that various changes and modifications can be made without departing from the inventive concept thereof, and these changes and modifications can be made without departing from the spirit and scope of the invention.
Claims (10)
1. A method for detecting the running safety of a echelon energy storage battery is characterized by comprising the following steps: which comprises the following steps:
s1) building a echelon energy storage system: selecting cells with deformation variation characteristics belonging to the same class to form a battery module, forming a battery cluster by a plurality of battery modules, and forming a echelon energy storage system by a plurality of battery clusters;
s2) calculating deformation parameters of each battery cell:
s21) acquiring deformation data of at least three surfaces of each battery cell, wherein the deformation data are respectively as follows: c x1 、C x2 、C x3 ；
S22) distributing corresponding weight coefficients, which are respectively k, to the deformation data according to whether electrodes exist on the surface of the electric core corresponding to the deformation data or not 1 、k 2 、k 3 ；
S23) deformation parameter C of battery cell x x Comprises the following steps:
s3) calculating deformation parameters of all battery cells in the battery unit t:
wherein N is the total number of the battery cells in the battery unit;
s4) calculating the dynamic change characteristic vector x of the M-dimensional deformation quantity of the battery unit t t ：
S41) constructing a deformation parameter curve according to the deformation parameters obtained in the step S3), and dividing the deformation parameter curve into M segments by taking the cell voltage variation delta U = U as a step length;
s42) calculating deformation parameters of each segmentA numerical slope of
S43) the dynamic variation characteristic vector of the M-dimensional deformation quantity of the battery unit is x t ＝[σ 1 ,σ 2 ,…,σ M ]T =1,2, \ 8230, wherein T is the total number of the battery cells;
s5) carrying out cluster analysis on the dynamic change characteristic vectors of the M dimensional deformation quantities corresponding to all the battery units by adopting a Gaussian regression model to obtain T classification results, and judging whether abnormality exists or not;
s51) calculating the deformation dynamic change characteristic vector x corresponding to the battery unit t t Posterior probability P (q) belonging to class k Gaussian curve tk ＝1|x t )：
wherein ,P(xt |μ j ,Σ j ) Is a multi-element Gaussian distribution, and has the characteristics of high temperature,
at the initial state, n k 、μ k 、Σ k Respectively obtaining mixing coefficients, mean vectors and covariance matrixes corresponding to parameters of K Gaussian mixture models of the echelon energy storage system constructed in the step S1) in the component content screening process;
s52) judges whether or not it belongs to the kth class: when q is tk If =1, then the deformation amount dynamic change eigenvector x of the battery unit t is represented t Belongs to the kth category; otherwise, the classification does not belong to the kth classification;
s53) repeating the steps S51) to S52) to obtain the classification results of the T battery units, wherein if the classification results of all the battery clusters are the same, the classification results are normal; if different classification results exist, alarming is carried out on the corresponding battery unit;
s6) recalculating a new mean vector mu according to the result of the step S5) k Coefficient of mixing pi k And covariance matrix sigma k As initial parameters in step S5); wherein,
and S7) repeating the steps S2) to S6), and continuously monitoring whether the battery units in the echelon energy storage system are abnormal or not.
2. The echelon energy storage battery operation safety detection method of claim 1, characterized in that: the battery unit is a battery cluster or a battery module.
3. The echelon energy storage battery operation safety detection method of claim 1, characterized in that: and the weight coefficient of the deformation data is equal to the ratio of the distribution area to the total area of the battery cell.
4. The echelon energy storage battery operation safety detection method according to claim 3, characterized in that: the calculation method of the distribution area comprises the following steps: if the deformation data correspond to the situation that no electrode exists on the surface of the electric core, calculating the distribution area according to 1 time of the area; if one electrode exists, the distribution area is calculated according to 2 times; if there are two electrodes, the dispensing area is calculated as a factor of 4.
5. The echelon energy storage battery operation safety detection method of claim 4, characterized in that:
wherein ,L1 The cell width; l is 2 The cell thickness is taken as the cell thickness; l is 3 Is the length of the battery core.
6. The echelon energy storage battery operation safety detection method as recited in claim 1, further comprising: in the step S41), M is:
7. the utility model provides a echelon energy storage battery operation safety inspection system which characterized in that: which comprises
The deformation sensing unit is coupled on at least three surfaces of each battery cell and is used for detecting deformation quantities of the battery cells and among the battery cells;
the voltage sensing unit is used for detecting the voltage of the positive electrode end and the negative electrode end of each battery cell; and
the judging unit is used for comprehensively judging whether the echelon energy storage system has abnormal deformation in the operation process; the deformation sensing units are electrically connected with the judging unit after being connected in series, and the voltage value acquired by the voltage sensing unit is uploaded to the judging unit; the decision unit is written with a program algorithm implementing the detection method as claimed in claim 1.
8. The echelon energy storage battery operational safety detection system of claim 7, wherein: the three surfaces include surfaces on which positive and negative electrodes of the cell are located.
9. The echelon energy storage battery operation safety detection system of claim 7, characterized in that: the three surfaces are selected from three of four surfaces around the battery core and comprise surfaces where positive and negative electrodes of the battery core are positioned; the deformation sensing units on the same battery core are arranged on the same height plane.
10. A echelon energy storage system, its characterized in that: the detection system comprises the detection system of claim 7, wherein each battery cell is provided with three deformation sensing units and two voltage sensing units; a plurality of battery cores are assembled to form a battery module, and each battery module is matched with one judgment unit; a plurality of battery modules form a battery cluster, and the plurality of battery clusters form the echelon energy storage system; the negative electrodes of all the battery modules are gathered to form a battery total negative electrode, the positive electrodes of all the battery modules are gathered to form a battery total positive electrode, and the battery total negative electrode and the battery total positive electrode are connected to the corresponding connecting ends of the energy storage converter; all the determination units are electrically connected to an energy management system and communicate with the energy management system.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103018681A (en) * | 2012-12-19 | 2013-04-03 | 上海交通大学 | Electric vehicle battery life decline and safe state detection technology based on deformation of battery cell in any shape |
| CN106415918A (en) * | 2014-06-30 | 2017-02-15 | 东洋橡胶工业株式会社 | Method for determining abnormality in battery pack, and device for determining abnormality in battery pack |
| CN107728079A (en) * | 2017-11-28 | 2018-02-23 | 西藏大学 | A kind of photovoltaic energy storage battery rapid detection system |
| CN110504501A (en) * | 2019-08-19 | 2019-11-26 | 国网河北省电力有限公司石家庄供电分公司 | Retired battery Gradient utilization method and system |
| CN112018461A (en) * | 2020-08-21 | 2020-12-01 | 常州瑞德丰精密技术有限公司 | Battery pack, electric vehicle and energy storage device |
| WO2021054716A1 (en) * | 2019-09-18 | 2021-03-25 | 주식회사 엠피에스티 | Abnormal state pre-sensing system using battery voltage data and temperature data |
| CN215681813U (en) * | 2021-07-26 | 2022-01-28 | 上海煦达新能源科技有限公司 | Parallel energy storage system |
| CN215768929U (en) * | 2021-06-23 | 2022-02-08 | 太原理工大学 | Hybrid energy storage battery state monitoring system |
| CN114171810A (en) * | 2021-12-03 | 2022-03-11 | 珠海迈巨微电子有限责任公司 | Battery deformation detection-based battery monitoring method and device and electronic equipment |
-
2022
- 2022-06-27 CN CN202210743905.2A patent/CN115144753B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103018681A (en) * | 2012-12-19 | 2013-04-03 | 上海交通大学 | Electric vehicle battery life decline and safe state detection technology based on deformation of battery cell in any shape |
| CN106415918A (en) * | 2014-06-30 | 2017-02-15 | 东洋橡胶工业株式会社 | Method for determining abnormality in battery pack, and device for determining abnormality in battery pack |
| CN107728079A (en) * | 2017-11-28 | 2018-02-23 | 西藏大学 | A kind of photovoltaic energy storage battery rapid detection system |
| CN110504501A (en) * | 2019-08-19 | 2019-11-26 | 国网河北省电力有限公司石家庄供电分公司 | Retired battery Gradient utilization method and system |
| WO2021054716A1 (en) * | 2019-09-18 | 2021-03-25 | 주식회사 엠피에스티 | Abnormal state pre-sensing system using battery voltage data and temperature data |
| CN112018461A (en) * | 2020-08-21 | 2020-12-01 | 常州瑞德丰精密技术有限公司 | Battery pack, electric vehicle and energy storage device |
| CN215768929U (en) * | 2021-06-23 | 2022-02-08 | 太原理工大学 | Hybrid energy storage battery state monitoring system |
| CN215681813U (en) * | 2021-07-26 | 2022-01-28 | 上海煦达新能源科技有限公司 | Parallel energy storage system |
| CN114171810A (en) * | 2021-12-03 | 2022-03-11 | 珠海迈巨微电子有限责任公司 | Battery deformation detection-based battery monitoring method and device and electronic equipment |
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